In photovoltaic (PV) array, the output power and the power–voltage ( P – V ) characteristic of PV array are totally dependent on the temperature and solar insolation. Therefore, if these atmospheric parameters fluctuate rapidly, then the maximum power point (MPP) of the P–V curve of PV array also fluctuates very rapidly. This rapid fluctuation of the MPP may be in accordance with the uniform shading of the PV panel or may be in accordance to the partially shaded due to the clouds, tall building, trees, and raindrops. However, in both cases, the MPP tracking (MPPT) is not only a nonlinear problem, this becomes a highly nonlinear problem, which solution is time bounded. Because the highly fluctuating atmospheric conditions change the P–V characteristic after every small time duration. This paper introduces a hybrid of “Jaya” and “differential evolution (DE)” (JayaDE) technique for MPPT in the highly fluctuating atmospheric conditions. This JayaDE algorithm is tested on MATLAB simulator and is verified on a developed hardware of the solar PV system, which consists of a single peak and many multiple peaks in the voltage–power curve. Moreover, the tracking ability is compared with the recent state of the art methods. The satisfactory steady-state and dynamic performances of this new hybrid technique under variable irradiance and temperature levels show the superiority over the state-of-the-art control methods.

This paper introduces a battery charging scheme from a solar photovoltaic (SPV) by using a single sensor-based maximum power point tracking (MPPT) strategy. Here, for quick and efficient tracking, a novel hybrid “Cauchy and Gaussian sine cosine optimization” (CGSCO) algorithm is proposed for MPPT, which is based on only a single current sensor. The main objective of the CGSCO algorithm is, maximum extraction of the power from SPV panel and efficiently charging the battery through maximizing the charging current of the battery. Due to the single sensor, the cost of the charging scheme is very low, as well as the algorithm complexity and computational burden are very less, so it can be easily implemented on the low-cost microcontroller. In this paper, a single current sensor-based battery charging scheme by CGSCO algorithm is tested on MATLAB simulator and verified on a developed hardware of the SPV system. The panel condition, with and without shaded as well as dynamic environmental condition (variable temperature and insolation), is considered during simulation as well as on hardware implementation. Moreover, the tracking ability is compared with the most recent state of the art techniques (Grey wolf optimization and Lagrange interpolation particle swarm optimization (LIPSO)) as well as compared with “CGSCO with the conventional dual (voltage and current) sensor-based MPPT scheme.” The efficient battery charging with quick MPPT by CGSCO algorithm w.r.t. all state of the art techniques as well as dual sensor-based MPPT scheme, in steady-state as well as in dynamic conditions meets the motive of the work.

This paper introduces a humpback whale hunting behavior inspired whale optimization with differential evolution (WODE) technique-based tracking algorithm for the maximum power point tracking in the dynamic as well as the steady-state conditions of a partially shaded solar photovoltaic (PV) system. This “WODE” technique is used for quick and oscillation-free tracking of the global best peak position in a few steps. The unique advantage of this algorithm for maximum power point tracking in partially shaded condition is as, it is free from common and generalized problems of other evolutionary techniques, like longer convergence duration, a large number of search particles, steady-state oscillation, heavy computational burden, etc., which creates power loss and oscillations in output. This hybrid algorithm is tested in MATLAB simulation and verified on a developed hardware of the solar PV system, which consists of multiple peaks in voltage-power curve. Moreover, the tracking ability is compared with the state-of-the-art methods. The satisfactory steady-state and dynamic performances of the new hybrid technique under variable irradiance and temperature levels show the superiority over the state-of-the-art control methods.

Expert, intelligent and robust automatic generation control (AGC) scheme is requisite for stable operation and control of power system (PS) integrated with renewable energy sources (RES) under sudden/random small load disturbances. Large frequency deviations appear if AGC capacity is inept to compensate for the imbalances of generation and demand. In this paper, a cascade‐fractional order ID with filter (C‐I ^λ D ^μ N) controller is proposed as an expert supplementary controller to promote AGC recital adequately in electric power systems incorporated with RES like solar, wind and fuel cells. The imperialist competitive algorithm is fruitfully exploited for optimizing the controller parameters. First, a 2‐area reheat thermal system is examined critically and then to authorize the worth of the proposed controller, the study is protracted to a 2‐area multi‐source hydro‐thermal system. The prominent benefits of C‐I ^λ D ^μ N controller with/without renewable energy sources consist of its great indolence to large load disturbances and superiority over various optimized classical/fuzzy controllers published recently. The sensitivity study validates the robustness of the recommended controller against ±20% deviations in the system parameters and random step load perturbations.

This paper deals with a new version of an incremental conductance algorithm for maximum power harvesting (MPH) from the solar photovoltaic array, which has inherent decision taking and self-adaptive ability. The working principle of self-adaptive incremental conductance (SAInC) algorithm is based on three consecutive operating points on the power-voltage characteristic. Out of three points, the first point is the driving point, which smartly detects the dynamic condition, as well as in normal condition, searches the maximum power peak (MPP) zone. Moreover, by using a triangular analogy, the rests two points decide the optimum operating position for next iteration, which is responsible for quick MPP tracking as well as good dynamic performance. Here, in every new iteration, the step-size is reduced by 90% from the previous step-size, which provides an oscillation-free steady-state performance. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on a experimental system. Moreover, performance of SAInC algorithm is compared with the popular and recent state of art methods. The satisfactory dynamic and steady-state performance with low complexity as well as low computational burden of SAInC algorithm shows the superiority over state of art methods.

This paper deals with a new version of perturb and observe tracking algorithm for maximum power extraction from the solar photovoltaic panel, which has self-predictive and decision taking ability. The working principle of self-predictive perturb and observe(SPP&O) algorithm is based on three consecutive operating points on the power-voltage characteristic. Out of three points, first two points, very smartly detects the dynamic condition, as well as in normal condition, quickly searches the maximum power point(MPP) region. Moreover, by using a circular analogy, all points decide the optimal operating position for next iteration, which is responsible for quick MPP tracking as well as improved dynamic performance. Here, in every new iteration, the step-size is reduced by 90% from the previous step-size, which provides an oscillation-free steady state performance. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on hardware prototype. Moreover, comparison between SPP&O algorithm and state of art methods is made. Its satisfactory dynamic and steady state behaviors with low algorithm complexity as well as the low computational burden of the SPP&O algorithm show the superiority over state of the art methods.